108 results about "Risk category" patented technology

The invention discloses a grassroots power supply enterprise compliance risk intelligent system and method based on big data analysis. The data acquisition unit is used for acquiring business operation data, historical violation records and policy and regulation update data of basic power supply enterprises to form a unified compliance data set. And the natural language processing unit performs text word segmentation and correlation analysis on the policy and regulation and violation record data, extracts key risk factors and labels compliance risk labels. And the risk feature construction unit performs multi-dimensional feature fusion on the business operation data and the compliance risk label data to generate a feature matrix for risk identification. And the intelligent risk assessment unit performs real-time analysis on the feature matrix by using a pre-trained machine learning model, identifies compliance risk categories and levels, and generates early warning information. According to the method, the accuracy of compliance risk identification is improved by using big data analysis and an intelligent algorithm, the compliance management cost of basic-level power supply enterprises is reduced, and the operation safety and compliance of the enterprises are improved.

The invention discloses an intelligent contract risk recognition method and system, and relates to the technical field of text recognition, and the method comprises the steps: extracting a semantic vector of a to-be-recognized file; obtaining a first risk identification result based on the semantic vector and the review list; performing semantic matching on the semantic vector and the review knowledge graph to obtain a potential risk; obtaining a first risk category based on the potential risk and the review knowledge spectrogram, obtaining a preset risk judgment rule based on the first risk category, and obtaining a second risk category based on the preset risk judgment rule; obtaining a second risk identification result based on the contract category and the second risk category; constructing a clause rule base, and obtaining a compliance result based on the semantic vector and the clause rule base; obtaining a complete result based on the semantic vector and the standardized contract template library; and obtaining a total risk identification result based on the above identification result, thereby solving the problems of low risk identification efficiency and low accuracy caused by the fact that an existing contract term risk identification method depends on the determination of the license experience of professionals.

The invention provides a risk behavior perception method and device based on deep learning, and the method comprises the steps: firstly collecting multi-source behavior data containing a network access record, a terminal operation log and an application program interface calling sequence in a preset time window of a target user, carrying out the spatial-temporal feature extraction of the multi-source behavior data, generating a behavior track feature vector, and carrying out the feature extraction of the behavior track feature vector; the method comprises the following steps: acquiring a network access record, analyzing the network access record through a protocol to obtain a network behavior feature vector, inputting the network behavior feature vector and the network behavior feature vector into a pre-trained risk assessment neural network for joint coding to obtain behavior risk probability distribution, determining a risk level according to a risk category exceeding a preset threshold, generating an early warning signal, and finally triggering a real-time alarm module according to the early warning signal. The risk description text and the risk relief strategy are sent to the safety management terminal, multi-source data comprehensive analysis is achieved, the risk is accurately evaluated, early warning is timely and effective, and the risk perception ability is improved.

The invention provides an intelligent analysis method and system for territorial space planning data, and relates to the technical field of data processing, and the method comprises the steps: recognizing an overlapping region through comparative analysis; performing buffer area analysis on the geographic information data, the land utilization data and the urban planning data, and creating buffer areas for rivers, roads and protection areas to identify conflict areas; according to the overlapping region and the conflict region, a corresponding territorial space planning scheme is found through iterative search; performing pattern recognition on the territorial space planning scheme to obtain a planning risk assessment result; according to the planning risk assessment result, the risks are divided into different categories, and each risk is quantified to obtain a specific score of each risk category; and obtaining an overall risk assessment result of the whole territorial space planning scheme according to the scores of the various risks. According to the invention, the accuracy and efficiency of risk assessment can be improved.

The invention relates to the technical field of computers, and discloses a model content security control method and system, electronic equipment and a storage medium, and the method comprises the steps: combining a received user input with a historical dialogue record of a current session to form a dialogue context; performing risk identification based on the dialogue context, generating at least one risk category and a corresponding initial risk value, and generating a risk integral corresponding to the current input according to a preset integral strategy; accumulating the risk points to accumulated risk points of the current session; comparing the accumulated risk integral with a dynamic risk threshold value obtained by calculation according to a reference threshold value, a logarithmic function attenuation item of the dialogue round and a user historical behavior adjustment item; and when the accumulated risk integral reaches or exceeds a dynamic risk threshold value, triggering a preset risk management and control measure. According to the method, the risk content can be effectively identified and controlled, attacks bypassing a security mechanism through slow induction can be defended, and the security boundary is not easy to detect.

The systems and methods disclosed herein receive alphanumeric characters defining operative boundaries for expected model use cases, along with operational data. The expected model use cases share common attributes, which are used by a first AI model to construct observed model use cases from the operational data. Each observed model use case includes features such as a text-based description, expected input and output,? AI model(s) generating the expected output from the input, and / or data supporting the AI models. For each observed model use case, a second AI model maps the alphanumeric characters and features to a risk category, selecting from multiple risk categories based on the level of risk associated with the features. The system identifies criteria for the observed model use case within the alphanumeric characters and generates gaps by comparing the criteria with the features of the observed model use case.

The systems and methods disclosed herein receive alphanumeric characters defining operative boundaries for expected model use cases, along with operational data. The expected model use cases share common attributes, which are used by a first AI model to construct observed model use cases from the operational data. Each observed model use case includes features such as a text-based description, expected input and output, AI model(s) generating the expected output from the input, and / or data supporting the AI models. For each observed model use case, a second AI model maps the alphanumeric characters and features to a risk category, selecting from multiple risk categories based on the level of risk associated with the features. The system identifies criteria for the observed model use case within the alphanumeric characters and generates gaps by comparing the criteria with the features of the observed model use case.

The invention discloses a smart city regional risk intensity determination method, system and device, and a storage medium. The method comprises the following steps: obtaining risk factor parameters of each device of a smart city at a current sampling moment; determining an equipment risk value of each piece of equipment at the current sampling moment according to the risk factor parameters; when the equipment risk value is greater than a preset threshold value, generating work order alarm data of each piece of equipment; generating a risk category of each device at the current sampling moment according to the work order alarm data; determining a risk coefficient of a target area according to the equipment risk value and the risk category; wherein the target area comprises a plurality of devices, and the risk coefficient is used for representing the risk intensity of the smart city area. The method can be widely applied to the technical field of artificial intelligence.

The invention discloses a call number risk identification method and device, electronic equipment and a computer program product. Obtaining a plurality of call tickets to be analyzed; analyzing the call data corresponding to the call number by using a weighted scoring model to obtain a risk score of the call number, the weighted scoring model performing weighted summation on parameter scores corresponding to multiple call parameters of the same call number by using a feature weight corresponding to each call parameter in advance; screening call numbers of which the risk scores accord with a preset scoring condition from call numbers corresponding to the plurality of call tickets to be analyzed to obtain risk numbers to be analyzed; and performing risk analysis on the risk behavior data corresponding to the to-be-analyzed risk number by using a preset classification model to obtain a number category to which the to-be-analyzed risk number belongs, the number category at least comprising a risk category to which the risk number belongs. According to the invention, the technical problem of poor efficiency of traditional number risk identification is solved.

One or more embodiments of the invention provide a content verification method and device, a storage medium and a computer program product. A question-answer pair can be obtained, wherein the question-answer pair comprises a user question and an answer provided by a question-answer system based on a first language model for the user question; then, a cue word can be constructed based on the question and answer pair and a pre-constructed risk category library, so that the cue word is utilized to guide a second language model to predict a target risk category to which an answer to be verified in the question and answer pair belongs based on the risk category library, and one or more risk categories are recorded in the risk category library; each risk category is defined; after the prediction result is obtained, the prediction result output by the second language model can be rechecked, and whether the answer to be checked has a risk is determined based on the recheck result.

The invention provides a sound barrier multi-risk fusion identification method and device based on deep learning, and relates to the field of data processing. The method comprises the following steps: acquiring detection data for target sound barrier risk detection; determining risk assessment data corresponding to the risk factors according to the detection data; carrying out data fusion on the risk assessment data by adopting a deep learning model to obtain risk category probability distribution; and determining whether the target sound barrier has a risk based on the risk category probability distribution. By implementing the technical scheme provided by the invention, the accuracy of sound barrier risk identification can be improved.

The invention relates to the technical field of artificial intelligence, and discloses a feature screening method and device based on artificial intelligence, equipment and a storage medium, and the method comprises the steps: obtaining a feature factor which affects the pricing of vehicle insurance; preprocessing the feature factors to obtain features influencing car insurance pricing and risk category labels of the features; through a support vector machine and an arrangement importance algorithm, according to the features and the risk category labels of the features, candidate features are screened out from the features, and the importance degrees of the candidate features are obtained; and through a quantum annealing algorithm, according to the risk category labels and the importance degrees of the candidate features, target features influencing vehicle insurance pricing are screened out from the candidate features. The method can be applied to feature screening in a financial vehicle insurance pricing scene, the efficiency and quality of feature screening can be remarkably improved, and the feature screening difficulty is effectively reduced.

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support functionality for identifying, quantifying, and mitigating risks. A dataset that includes a plurality of data items associated with one or more entities is generated and a set of rules is executed against the dataset to determine a sentiment for each data item of the plurality of data items. The dataset may be evaluated against a machine learning model configured to produce a set of risk categorizations that associate each of the plurality of data items with a risk category. Scoring metrics are generated for each of the data items based at least in part on the evaluating. The scoring metrics may account for an impact of dependencies and capabilities of the one or more entities on risks corresponding to the risk categorizations. A data structure may be created and displayed to the user based on the scoring metrics, where the data structure is configured to quantify risks identified for each of the entities and to identify actions configured to mitigate the risks for each entity.

The invention discloses a urinary surgery preoperative risk assessment method and system, and the method comprises the following steps: dividing a urinary surgery operation process into a plurality of operation units, and each operation unit represents a key step in the operation process; performing preliminary risk assessment on each operation unit based on historical data, determining a risk value of the operation unit, and dividing the operation unit into three risk categories of low risk, medium risk and high risk according to the risk value of the operation unit; for a low-risk operation unit, directly calling a text template in a historical database, and generating standardized prompt information; for the medium-risk operation unit, constructing a multi-dimensional thermodynamic rendering diagram in combination with the specific medical record and image data of the patient; for a high-risk operation unit, on the basis of generating the thermal rendering diagram, a dynamic interactive anatomical model is constructed, a detailed risk assessment result is output, and the method has the advantages that the operation operation unit can be subdivided, and the risk can be quantitatively assessed.

The invention discloses a rural finance-oriented LSTM-auto-encoder hybrid risk early warning method, and particularly relates to the technical field of risk prediction. Rural financial risk event historical sequence features are extracted by using agricultural production cycle and rural policy cycle information, and an agricultural policy drift feature set is formed; according to the method, asynchronous memory state coding of multi-scale features is realized by constructing a multi-window layered long-short-term memory network sub-network, and the multi-scale features are input into an auto-encoder after being subjected to weighted aggregation, so that risk event time sequence reconstruction is completed, and a reconstruction error sequence is obtained; determining a critical threshold value of a reconstruction error by adopting an error sensitive threshold value generation strategy based on causal traceability in combination with a rural financial historical risk real event; and reconstructing an error sequence according to the critical threshold value to implement discrimination, outputting a risk event early warning result, and determining a specific risk category and a corresponding influence factor. Accurate identification and effective early warning of rural financial risks are realized.

The systems and methods disclosed herein receive alphanumeric characters defining operative boundaries for expected model use cases, along with operational data. The expected model use cases share common attributes, which are used by a first AI model to construct observed model use cases from the operational data. Each observed model use case includes features such as a text-based description, expected input and output,? AI model(s) generating the expected output from the input, and / or data supporting the AI models. For each observed model use case, a second AI model maps the alphanumeric characters and features to a risk category, selecting from multiple risk categories based on the level of risk associated with the features. The system identifies criteria for the observed model use case within the alphanumeric characters and generates gaps by comparing the criteria with the features of the observed model use case.

According to an embodiment, disclosed is a system comprising a processor configured to define, one or more risk categories for monitoring a risk associated with a clinical trial, wherein the risk categories comprise one or more risk elements; calculate, a first risk profile data of the risk categories based on a risk factor and a weighting assigned to the risk elements; generate, a machine learning (ML) model; train, the ML model; receive, a second risk profile data; analyse, the second risk profile data to identify a pattern based on the first risk profile data using a database; predict, an overall risk score; recommend, one or more of a type of monitoring, a level of monitoring, and the overall risk score; and wherein the ML model comprises a feed-back layer to enable continuous learning and improve the prediction of the overall risk score and monitoring decisions of the clinical trial.

This application provides a data processing method, apparatus, device, and medium. The method includes: acquiring a first text containing business text data; performing a risk assessment on the first text to obtain a risk category result corresponding to the first text; if the risk category result is a first risk category, acquiring keywords of the business text data and searching for the keywords in a standard database; if a request text matching the keywords is found in the standard database, determining the feedback text corresponding to the request text as the business processing result corresponding to the business text data; if no request text matching the keywords is found in the standard database, performing text search processing on the business text data in a target knowledge graph to obtain a business processing result matching the business text data. Implementing this application embodiment can improve the security of text data.

The present invention provides a risk behavior perception method and device based on deep learning. First, multi-source behavior data including network access records, terminal operation logs, and application program interface call sequences within a preset time window of a target user is collected. Then, spatio-temporal features of the multi-source behavior data are extracted to generate behavior trajectory feature vectors, and network behavior feature vectors are obtained by protocol parsing of the network access records. After that, the two are input into a pre-trained risk assessment neural network for joint encoding to obtain a behavior risk probability distribution. Then, the risk level is determined based on the risk categories exceeding the preset threshold, and a warning signal is generated. Finally, according to the warning signal, a real-time alarm module is triggered to send a risk description text and a risk mitigation strategy to a security management terminal, realizing comprehensive analysis of multi-source data, accurately evaluating risks and giving timely and effective warnings, and improving the risk perception ability.

The invention relates to the field of risk classification prediction, and provides a medical institution risk classification prediction method and device based on an attention mechanism, and the method comprises the steps: carrying out the analysis of a to-be-classified file through a medical risk classification prediction model, and obtaining a feature map and different risk categories; calculating the weight of each pixel point in the feature map through a medical risk classification prediction model, and adjusting the weight of each pixel point according to the calculated weight to obtain a weighted feature map; calculating the weight of each channel in the feature map through a medical risk classification prediction model, and adjusting the weight of each channel according to the calculated weight to obtain an adjusted feature map; features are extracted based on an attention mechanism, the probabilities of different risk categories are calculated through a normalization function, and the risk category corresponding to the maximum probability value serves as a prediction result to be returned; accurate identification and multi-modal identification of medical institution terminologies are realized, and the accuracy and automatic processing of risk classification and identification are improved.

This invention discloses a method for quantitatively evaluating the edge properties of complex scenarios based on a multi-category risk field model, belonging to the field of autonomous driving technology. The method includes: analyzing and classifying temporal scene elements based on the PEGASUS system; constructing a large-scale risk field for unidentified scene elements; constructing a medium-scale risk field for low-confidence identified scene elements; constructing an anisotropic driving behavior risk field for high-confidence identified scene elements; superimposing multiple risk fields within the interaction area; and quantifying the edge properties of the scenario using the minimum travel cost of the main vehicle's path. This invention achieves accurate quantification of the edge properties of complex scenarios by differentially modeling multiple risk sources and superimposing field strengths, utilizing the optimal path cost. It solves the problem that existing methods struggle to uniformly characterize multiple risk categories and overall travel costs, providing strong support for risk assessment and edge scenario mining for autonomous vehicles.

The invention discloses a risk assessment device and method for a live broadcast advertisement with goods. The risk assessment device for the live broadcast advertisement with goods comprises a shape sub-sequence determining module, a shape sub-sequence determining module and a shape sub-sequence determining module, wherein the shape sub-sequence determining module is used for screening a most representative shape sub-sequence for each risk category from time sequence data for assessing the risk of the live broadcast advertisement with goods; the dynamic feature extraction module determines key dynamic time sequence feature data of the time sequence data based on the time sequence data and the most representative shape subsequence of each risk category; the general feature extraction module extracts general feature data of the time sequence data; the static feature extraction module extracts static feature data of the time sequence data; the feature fusion module performs fusion processing on the key dynamic time sequence feature data, the general feature data and the static feature data to obtain fused feature data; and the classifier module performs risk category assessment based on the fused feature data to obtain the risk category of the live broadcast advertisement with goods.

The invention provides an object classification method and device, computer equipment and a storage medium, belongs to the technical field of computers, and can be applied to various scenes such as cloud technology, artificial intelligence, intelligent traffic and auxiliary driving. The method comprises the following steps: for any to-be-classified user object, obtaining scaling coefficients of a plurality of risk categories; for any risk category, a plurality of first intervals of the user object under the risk category are determined, and the first intervals are the distances between the user object data of the user object and the subclass center of each subclass of the risk category; determining a plurality of second intervals of the user object under the risk category according to the plurality of first intervals and scaling coefficients of the risk category; and determining the risk category to which the minimum second interval of the user object in the plurality of risk categories belongs as the category to which the user object belongs. According to the technical scheme, the problem of unfair classification caused by the difference of the average distance between the sub-class centers can be avoided, so that the classification accuracy is improved.

The embodiment of the invention discloses a training method and device for a text risk classification system and a training method and device for a business object classification system. The text risk classification system comprises a base model and K classification heads corresponding to K risk categories, and the training method of the text risk classification system comprises the steps that firstly, a training text set is acquired, and any first training text is associated with a first text label indicating the risk category to which the first training text belongs; then, inputting the first training text into the base model to obtain a first output; then, respectively inputting the first output into the K classification heads to obtain K classification probabilities corresponding to the output; and then, based on the K classification probabilities and the first text label, training the K classification heads. A text risk classification system trained according to the method can quickly adapt to changes of risk category labels.

The present invention discloses a method and system for intelligently identifying contract risks, which relate to the field of text recognition technology. The method includes: extracting semantic vectors of files to be identified; obtaining a first risk identification result based on the semantic vectors and a review list; semantically matching the semantic vectors with a review knowledge graph to obtain potential risks; obtaining a first risk category based on the potential risks and the review knowledge graph, obtaining a preset risk judgment rule based on the first risk category, and obtaining a second risk category based on the preset risk judgment rule; obtaining a second risk identification result based on the contract category and the second risk category; constructing a clause rule library, obtaining a compliance result based on the semantic vectors and the clause rule library; obtaining a complete result based on the semantic vectors and a standardized contract template library; and obtaining a total risk identification result based on the above identification results, so as to solve the problem that the existing contract clause risk identification method relies on the professional experience of professionals to make judgments, resulting in low risk identification efficiency and low accuracy.

The invention relates to the field of data request processing, and discloses an asset data grouping method, which comprises the following steps: acquiring a plurality of risk indexes corresponding to asset data according to identifiers of the asset data, and a preset mapping relationship between the risk indexes and a classification decision tree model; extracting a first key feature and a second key feature of the asset data, and obtaining a classification decision tree model corresponding to each risk index of the asset data; inputting the first key feature and the second key feature into a classification decision tree model, performing path retrieval along a tree structure of the classification decision tree model to obtain a risk category of the asset data, and obtaining a data grouping rule of the asset data from a preset data grouping rule base according to the risk category; and matching the first key feature and the second key feature by using a data grouping rule to obtain a data group to which the asset data belongs, and storing the asset data in the data group. According to the invention, automatic asset data grouping is realized, and the grouping efficiency and precision are improved.

This invention relates to methods, security devices, computer programs and computer-readable storage media for initiating a security action for a component of an industrial system whereby the security action reduces or completely mitigates a vulnerability or a weak configuration of the component and / or of the industrial system, with the following steps: - Providing a respective property information for one or several component properties of the component; - Providing a risk information for a dedicated combination of the component property and risk category, whereby the risk information provides a risk of the property information of the component property in respect to the risk category; - Determining a joint risk information for the risk category by generating a risk value based on one or more of the risk information associated to the risk category; - Determining a component-based risk score based on the respective joint risk information of the several risk categories; - Initiating the security action if the component-based risk score indicates a required action.

The invention discloses an SQL statement intelligent auditing and risk early warning method for financial services, and relates to the technical field of financial service intelligent auditing, and the method comprises the following steps: 1, defining a financial SQL core risk type and a quantitative auditing dimension; 2, a full-dimensional sensing module is constructed, multi-source data related to the SQL to be audited are collected, and the multi-source data at least comprise SQL statement data, database operation environment data, financial service rule data, supervision compliance data and historical SQL execution log data. According to the method, financial SQL auditing scene elements are mapped into four types of element individuals of SQL, risk, compliance and business, association strength among the individuals is quantified in combination with a connection weight algorithm, a virtual social network is constructed, four types of core risks of multi-risk-dimension collaborative judgment, sensitive data coverage, compliance authorization, business logic and performance overload are realized, and the risk level of the financial SQL auditing scene is improved. And the one-sidedness of a traditional static rule is avoided.

The invention belongs to the technical field of artificial intelligence and natural language processing, and provides a customer service quality inspection system based on large language model distillation and a deployment method thereof. The method comprises the steps that multiple groups of first annotation data are obtained, and the data are generated through risk annotation of multiple groups of historical dialogues between customer service staff and customers and comprise dialogue information and corresponding risk category annotations; inputting each group of first annotation data into a trained first semantic model, and outputting a first probability label (namely a first quality inspection result) containing semantic judgment logic and different quality inspection category probability distribution information; and in combination with the plurality of first probability tags and the first annotation data, training to obtain a second semantic model of which the parameter scale is significantly smaller than that of the first semantic model. According to the method, the quality inspection efficiency can be improved while the quality inspection effect is guaranteed, and the hardware resource cost of model deployment is reduced.

A method for threat detection may include obtaining data that is related to a series of digital activities performed with accounts on a channel through which employees of an enterprise can communicate with other employees of the enterprise or accounts external to the enterprise. The method may include parsing the data to identify an attribute of each digital activity. The method may include generating one or more metrics indicative of a threat posed by a respective digital activity of the series of digital activities. The method may include generating a plurality of digital profiles for at least some of the employees of the enterprise based on the series of digital activities and comprising the one or more metrics. The method may include generating a graphical user interface indicating a risk category associated with at least some of the digital activities of the series of digital activities.